Surviving Fraction (Sf2) And Clonogen Cell Density (Ccd) Are The Dominant Predictors Of Tumor Control Probability (Tcp) In High Grade Glioma

Purpose/Objective(s)High grade glioma has a poor prognosis despite treatment with surgery followed by adjuvant chemotherapy and radiation. TCP modeling to predict tumor responses uses variables such as the α/β ratio, CCD and SF2 but their exact value is not known and may vary amongst patients or even amongst different clonogens or regions within the same tumor. In this study, we used various combinations of α/β, SF2 and CCD to determine TCP and identify their relative contributions in radio resistance for high grade glioma patients treated with IMRT.Materials/MethodsIMRT plans were generated for twenty patients with high grade glioma at the New York University Medical Center. The prescribed dose was 59.4 Gy at 1.8 Gy per fraction. Using the Niemierko method and the Munro-Gilbert hypothesis, TCP was calculated for α/β of 3, 5, 8 and 10, SF2 of 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and CCD of 100 to 2 × 108 cells/cc.ResultsSF2 was the major determinant of TCP. Specifically, average TCP was above 99% for SF2 of 0.3 and below 1% for SF2 of 0.8 irrespective of CCD. Inverse relationship between CCD and TCP was observed for SF2 of 0.4 to 0.7. Specifically, progressively increasing CCD values from 100 to 2 × 108 cells/cc resulted in TCP reduction from 100% to 89.8% for SF2 of 0.4, 100% to less than 1% for SF2 of 0.5, 99.7% to 0% for SF2 of 0.6 and from 75% to 0% for SF2 of 0.7. Smaller TCP decreases were noted with decreasing α/β ratios.ConclusionsTCP modeling predicts that SF2 is the dominant predictor of radioresistance followed by CCD. Relating clinical and molecular prognostic indicators to SF2, α/β and CCD will improve our ability to more precisely model response to therapy in high grade glioma. Combining TCP modeling with functional imaging will allow us to map tumor regions of higher grade and/or density and selectively intensify therapy using higher radiation doses with or without targeted agents. This will optimize target coverage while minimizing normal tissue damage and may improve the outcome of patients with high grade glioma. Purpose/Objective(s)High grade glioma has a poor prognosis despite treatment with surgery followed by adjuvant chemotherapy and radiation. TCP modeling to predict tumor responses uses variables such as the α/β ratio, CCD and SF2 but their exact value is not known and may vary amongst patients or even amongst different clonogens or regions within the same tumor. In this study, we used various combinations of α/β, SF2 and CCD to determine TCP and identify their relative contributions in radio resistance for high grade glioma patients treated with IMRT. High grade glioma has a poor prognosis despite treatment with surgery followed by adjuvant chemotherapy and radiation. TCP modeling to predict tumor responses uses variables such as the α/β ratio, CCD and SF2 but their exact value is not known and may vary amongst patients or even amongst different clonogens or regions within the same tumor. In this study, we used various combinations of α/β, SF2 and CCD to determine TCP and identify their relative contributions in radio resistance for high grade glioma patients treated with IMRT. Materials/MethodsIMRT plans were generated for twenty patients with high grade glioma at the New York University Medical Center. The prescribed dose was 59.4 Gy at 1.8 Gy per fraction. Using the Niemierko method and the Munro-Gilbert hypothesis, TCP was calculated for α/β of 3, 5, 8 and 10, SF2 of 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and CCD of 100 to 2 × 108 cells/cc. IMRT plans were generated for twenty patients with high grade glioma at the New York University Medical Center. The prescribed dose was 59.4 Gy at 1.8 Gy per fraction. Using the Niemierko method and the Munro-Gilbert hypothesis, TCP was calculated for α/β of 3, 5, 8 and 10, SF2 of 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and CCD of 100 to 2 × 108 cells/cc. ResultsSF2 was the major determinant of TCP. Specifically, average TCP was above 99% for SF2 of 0.3 and below 1% for SF2 of 0.8 irrespective of CCD. Inverse relationship between CCD and TCP was observed for SF2 of 0.4 to 0.7. Specifically, progressively increasing CCD values from 100 to 2 × 108 cells/cc resulted in TCP reduction from 100% to 89.8% for SF2 of 0.4, 100% to less than 1% for SF2 of 0.5, 99.7% to 0% for SF2 of 0.6 and from 75% to 0% for SF2 of 0.7. Smaller TCP decreases were noted with decreasing α/β ratios. SF2 was the major determinant of TCP. Specifically, average TCP was above 99% for SF2 of 0.3 and below 1% for SF2 of 0.8 irrespective of CCD. Inverse relationship between CCD and TCP was observed for SF2 of 0.4 to 0.7. Specifically, progressively increasing CCD values from 100 to 2 × 108 cells/cc resulted in TCP reduction from 100% to 89.8% for SF2 of 0.4, 100% to less than 1% for SF2 of 0.5, 99.7% to 0% for SF2 of 0.6 and from 75% to 0% for SF2 of 0.7. Smaller TCP decreases were noted with decreasing α/β ratios. ConclusionsTCP modeling predicts that SF2 is the dominant predictor of radioresistance followed by CCD. Relating clinical and molecular prognostic indicators to SF2, α/β and CCD will improve our ability to more precisely model response to therapy in high grade glioma. Combining TCP modeling with functional imaging will allow us to map tumor regions of higher grade and/or density and selectively intensify therapy using higher radiation doses with or without targeted agents. This will optimize target coverage while minimizing normal tissue damage and may improve the outcome of patients with high grade glioma. TCP modeling predicts that SF2 is the dominant predictor of radioresistance followed by CCD. Relating clinical and molecular prognostic indicators to SF2, α/β and CCD will improve our ability to more precisely model response to therapy in high grade glioma. Combining TCP modeling with functional imaging will allow us to map tumor regions of higher grade and/or density and selectively intensify therapy using higher radiation doses with or without targeted agents. This will optimize target coverage while minimizing normal tissue damage and may improve the outcome of patients with high grade glioma.